{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "vscode": {
     "languageId": "rust"
    }
   },
   "outputs": [],
   "source": [
    ":dep ndarray=\"0.15.1\""
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "https://blog.csdn.net/wsp_1138886114/article/details/108635600\n",
    "https://www.cnblogs.com/mxnote/articles/17459015.html\n",
    "\n",
    "ndarray-linalg 用于线性代数运算；\n",
    "ndarray-rand 用于产生随机数；\n",
    "ndarray-stats 用于统计计算；\n",
    "首先，ndarray是专门为处理n维数组（矩阵）而设计的，里面包含了很多数学运算，比如矩阵相乘、矩阵求逆等。\n",
    "其次，ndarray支持SIMD（Single Instruction Multiple Data），可以进一步提升计算性能。\n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "vscode": {
     "languageId": "rust"
    }
   },
   "outputs": [],
   "source": [
    "use ndarray::prelude::*;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "vscode": {
     "languageId": "rust"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1D array: \t[1.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[6], strides=[1], layout=CFcf (0xf), const ndim=1\n"
     ]
    }
   ],
   "source": [
    "let arr1 = array![1., 2., 3., 4., 5., 6.];\n",
    "println!(\"1D array: \\t{:?}\", arr1);\n",
    "\n",
    "let ls1 = [1., 2., 3., 4., 5., 6.];\n",
    "println!(\"1D list: \\t{:?}\", ls1);\n",
    "\n",
    "let vec1 = vec![1., 2., 3., 4., 5., 6.];\n",
    "println!(\"1D vector: \\t{:?}\", vec1);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "vscode": {
     "languageId": "rust"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1D array: \t[2, 4.2, 6.3, 8, 10, 12]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1D list: \t[1.0, 2.0, 3.0, 4.0, 5.0, 6.0]\n"
     ]
    }
   ],
   "source": [
    "\n",
    "let arr2 = array![1., 2.2, 3.3, 4., 5., 6.];\n",
    "let arr3 = arr1 + arr2;\n",
    "println!(\"1D array: \\t{}\", arr3);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "对比rust数据类型做加法\n",
    "ndarray array 如果形状相同，可以直接相加\n",
    "rust list 需要循环遍历\n",
    "rust vec 需要zip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "vscode": {
     "languageId": "rust"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1D array: \t[2, 4.2, 6.3, 8, 10, 12]\n",
      "1D list: \t[1.0, 4.2, 6.3, 8.0, 10.0, 12.0]\n",
      "1D vec: \t[2.0, 4.2, 6.3, 8.0, 10.0, 12.0]\n"
     ]
    }
   ],
   "source": [
    "let arr1 = array![1., 2., 3., 4., 5., 6.];\n",
    "\n",
    "let arr2 = array![1., 2.2, 3.3, 4., 5., 6.];\n",
    "// println!(\"arr1 1D array: \\t{}\", arr1);\n",
    "let arr3 = arr1 + arr2;\n",
    "println!(\"1D array: \\t{}\", arr3);\n",
    "\n",
    "let ls2 = [1., 2.2, 3.3, 4., 5., 6.];\n",
    "let mut ls3 = ls1.clone();\n",
    "for i in 1..ls2.len(){\n",
    "    ls3[i] = ls1[i] + ls2[i];\n",
    "}\n",
    "println!(\"1D list: \\t{:?}\", ls3);\n",
    "\n",
    "let vec2 = vec![1., 2.2, 3.3, 4., 5., 6.];\n",
    "let vec3: Vec<f64> = vec1.iter().zip(vec2.iter()).map(|(&e1, &e2)| e1 + e2).collect();\n",
    "println!(\"1D vec: \\t{:?}\", vec3);\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "vscode": {
     "languageId": "rust"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 1\n",
      "1 2\n",
      "2 3\n",
      "3 4\n",
      "4 5\n",
      "5 6\n",
      "i:1\n",
      "i:2\n",
      "i:3\n",
      "i:4\n",
      "i:5\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "()"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "let arr = [1,2,3,4,5,6];\n",
    "for (i, v) in arr.iter().enumerate() {\n",
    "    println!(\"{} {}\", i, v);\n",
    "}\n",
    "\n",
    "// .. 进行范围切片\n",
    "for i in 1..arr.len() {  \n",
    "    println!(\"i:{}\", i);\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ndarray提供多种方法创建和实例化二维数组\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "vscode": {
     "languageId": "rust"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2D array:\n",
      "[[2, 3, 4],\n",
      " [5, 6, 7]]\n"
     ]
    }
   ],
   "source": [
    "let arr4 = array![[1., 2., 3.], [ 4., 5., 6.]]; // (2, 3) 的二维数组\n",
    "let arr5 = Array::from_elem((2, 1), 1.); // (2,1) 通过广播实现对每一行中每一列加一\n",
    "// println!(\"arr5:{}\", arr5);\n",
    "let arr6 = arr4 + arr5;\n",
    "println!(\"2D array:\\n{}\", arr6);\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "上面代码展示了2种创建二维数组的方法。\n",
    "\n",
    "array!宏需要指定数组中的所有元素\n",
    "Array::from_elem需要我们给定Shape（在上面案例中是（2，1））和填充元素（在上面案例中是1.0），该方法会用给定元素填充指定大小的二维数组\n",
    "ndarray还提供了快速创建常用二维数组（矩阵）的方法。看下面代码："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "vscode": {
     "languageId": "rust"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0, 0, 0],\n",
      " [0, 0, 0]]\n"
     ]
    }
   ],
   "source": [
    "let arr7 =  Array::<f64, _>::zeros(arr6.raw_dim());\n",
    "let arr8 = arr6 * arr7;\n",
    "println!(\"{}\", arr8);\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Array::zeros(Shape)用0填充指定Shape大小的矩阵。\n",
    "\n",
    "这里的<f64, _>要解释一下，因为Rust有着严格的类型，编译器有时无法通过0推断出数据类型，所以需要我们显式地告诉编译器这里填充的数据是什么类型。后面的_表示让编译器推断这里的数据类型，此处的数据类型是Shape。\n",
    "\n",
    ".raw_dim()方法我想你已经猜到了它的作用，没错，就是返回矩阵的维度（dimension）。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "vscode": {
     "languageId": "rust"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "[[1, 0, 0],\n",
      " [0, 1, 0],\n",
      " [0, 0, 1]]\n"
     ]
    }
   ],
   "source": [
    "let identity: Array2<f64> = Array::eye(3);\n",
    "println!(\"\\n{}\", identity);\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "eye方法需要我们传入矩阵大小，因为单位矩阵一定是方阵，所以只需要传一个整数即可。这里要注意的是identity的类型我们明确定义为Array2类型，Array2是ArrayBase子类，表示一个二位数组。\n",
    "\n",
    "单位矩阵（数学上一般用 I表示）有很多性质，比如任何矩阵乘以单位矩阵都等于它本身： A ⋅ I = A = I ⋅ A A · I = A = I · A A⋅I=A=I⋅A，就像我们经典代数中的1一样。我们可以验证一下单位矩阵的这个性质。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "vscode": {
     "languageId": "rust"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1, 0, 0],\n",
      " [0, 5, 0],\n",
      " [0, 0, 9]]\n"
     ]
    }
   ],
   "source": [
    "let arr9 = array![[1., 2., 3.], \n",
    "                  [ 4., 5., 6.], \n",
    "                  [7., 8., 9.]];\n",
    "let arr10 = arr9 * identity; // 对应元素相乘\n",
    "println!(\"{}\", arr10);\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "输出结果似乎不是arr9本身。这是因为矩阵运算中*和·不是一回事。*也叫标量乘法，是将两个矩阵对应位置元素相乘。而·是将前一个矩阵的行向量与后一个矩阵的列向量相乘。因此·对矩阵的维度有要求，且不满足交换律。相比*,·乘在机器学习中用的更多，ndarray提供了dot()函数来计算·乘。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "vscode": {
     "languageId": "rust"
    }
   },
   "outputs": [
    {
     "ename": "Error",
     "evalue": "cannot find value `arr9` in this scope",
     "output_type": "error",
     "traceback": [
      "\u001b[31m[E0425] Error:\u001b[0m cannot find value `arr9` in this scope",
      "   \u001b[38;5;246m╭\u001b[0m\u001b[38;5;246m─\u001b[0m\u001b[38;5;246m[\u001b[0mcommand_21:1:1\u001b[38;5;246m]\u001b[0m",
      "   \u001b[38;5;246m│\u001b[0m",
      " \u001b[38;5;246m1 │\u001b[0m \u001b[38;5;249ml\u001b[0m\u001b[38;5;249me\u001b[0m\u001b[38;5;249mt\u001b[0m\u001b[38;5;249m \u001b[0m\u001b[38;5;249ma\u001b[0m\u001b[38;5;249mr\u001b[0m\u001b[38;5;249mr\u001b[0m\u001b[38;5;249m1\u001b[0m\u001b[38;5;249m1\u001b[0m\u001b[38;5;249m:\u001b[0m\u001b[38;5;249m \u001b[0m\u001b[38;5;249mA\u001b[0m\u001b[38;5;249mr\u001b[0m\u001b[38;5;249mr\u001b[0m\u001b[38;5;249ma\u001b[0m\u001b[38;5;249my\u001b[0m\u001b[38;5;249m2\u001b[0m\u001b[38;5;249m<\u001b[0m\u001b[38;5;249mf\u001b[0m\u001b[38;5;249m6\u001b[0m\u001b[38;5;249m4\u001b[0m\u001b[38;5;249m>\u001b[0m\u001b[38;5;249m \u001b[0m\u001b[38;5;249m=\u001b[0m\u001b[38;5;249m \u001b[0m\u001b[38;5;54ma\u001b[0m\u001b[38;5;54mr\u001b[0m\u001b[38;5;54mr\u001b[0m\u001b[38;5;54m9\u001b[0m\u001b[38;5;249m.\u001b[0m\u001b[38;5;249md\u001b[0m\u001b[38;5;249mo\u001b[0m\u001b[38;5;249mt\u001b[0m\u001b[38;5;249m(\u001b[0m\u001b[38;5;249m&\u001b[0m\u001b[38;5;249mi\u001b[0m\u001b[38;5;249md\u001b[0m\u001b[38;5;249me\u001b[0m\u001b[38;5;249mn\u001b[0m\u001b[38;5;249mt\u001b[0m\u001b[38;5;249mi\u001b[0m\u001b[38;5;249mt\u001b[0m\u001b[38;5;249my\u001b[0m\u001b[38;5;249m)\u001b[0m\u001b[38;5;249m;\u001b[0m",
      " \u001b[38;5;240m  │\u001b[0m                          \u001b[38;5;54m─\u001b[0m\u001b[38;5;54m─\u001b[0m\u001b[38;5;54m┬\u001b[0m\u001b[38;5;54m─\u001b[0m  ",
      " \u001b[38;5;240m  │\u001b[0m                            \u001b[38;5;54m╰\u001b[0m\u001b[38;5;54m─\u001b[0m\u001b[38;5;54m─\u001b[0m\u001b[38;5;54m─\u001b[0m error: cannot find value `arr9` in this scope",
      " \u001b[38;5;240m  │\u001b[0m                            \u001b[38;5;100m│\u001b[0m   ",
      " \u001b[38;5;240m  │\u001b[0m                            \u001b[38;5;100m╰\u001b[0m\u001b[38;5;100m─\u001b[0m\u001b[38;5;100m─\u001b[0m\u001b[38;5;100m─\u001b[0m help: a local variable with a similar name exists: `arr`",
      "\u001b[38;5;246m───╯\u001b[0m"
     ]
    },
    {
     "ename": "Error",
     "evalue": "cannot find value `identity` in this scope",
     "output_type": "error",
     "traceback": [
      "\u001b[31m[E0425] Error:\u001b[0m cannot find value `identity` in this scope",
      "   \u001b[38;5;246m╭\u001b[0m\u001b[38;5;246m─\u001b[0m\u001b[38;5;246m[\u001b[0mcommand_21:1:1\u001b[38;5;246m]\u001b[0m",
      "   \u001b[38;5;246m│\u001b[0m",
      " \u001b[38;5;246m1 │\u001b[0m \u001b[38;5;249ml\u001b[0m\u001b[38;5;249me\u001b[0m\u001b[38;5;249mt\u001b[0m\u001b[38;5;249m \u001b[0m\u001b[38;5;249ma\u001b[0m\u001b[38;5;249mr\u001b[0m\u001b[38;5;249mr\u001b[0m\u001b[38;5;249m1\u001b[0m\u001b[38;5;249m1\u001b[0m\u001b[38;5;249m:\u001b[0m\u001b[38;5;249m \u001b[0m\u001b[38;5;249mA\u001b[0m\u001b[38;5;249mr\u001b[0m\u001b[38;5;249mr\u001b[0m\u001b[38;5;249ma\u001b[0m\u001b[38;5;249my\u001b[0m\u001b[38;5;249m2\u001b[0m\u001b[38;5;249m<\u001b[0m\u001b[38;5;249mf\u001b[0m\u001b[38;5;249m6\u001b[0m\u001b[38;5;249m4\u001b[0m\u001b[38;5;249m>\u001b[0m\u001b[38;5;249m \u001b[0m\u001b[38;5;249m=\u001b[0m\u001b[38;5;249m \u001b[0m\u001b[38;5;249ma\u001b[0m\u001b[38;5;249mr\u001b[0m\u001b[38;5;249mr\u001b[0m\u001b[38;5;249m9\u001b[0m\u001b[38;5;249m.\u001b[0m\u001b[38;5;249md\u001b[0m\u001b[38;5;249mo\u001b[0m\u001b[38;5;249mt\u001b[0m\u001b[38;5;249m(\u001b[0m\u001b[38;5;249m&\u001b[0m\u001b[38;5;54mi\u001b[0m\u001b[38;5;54md\u001b[0m\u001b[38;5;54me\u001b[0m\u001b[38;5;54mn\u001b[0m\u001b[38;5;54mt\u001b[0m\u001b[38;5;54mi\u001b[0m\u001b[38;5;54mt\u001b[0m\u001b[38;5;54my\u001b[0m\u001b[38;5;249m)\u001b[0m\u001b[38;5;249m;\u001b[0m",
      " \u001b[38;5;240m  │\u001b[0m                                    \u001b[38;5;54m─\u001b[0m\u001b[38;5;54m─\u001b[0m\u001b[38;5;54m─\u001b[0m\u001b[38;5;54m─\u001b[0m\u001b[38;5;54m┬\u001b[0m\u001b[38;5;54m─\u001b[0m\u001b[38;5;54m─\u001b[0m\u001b[38;5;54m─\u001b[0m  ",
      " \u001b[38;5;240m  │\u001b[0m                                        \u001b[38;5;54m╰\u001b[0m\u001b[38;5;54m─\u001b[0m\u001b[38;5;54m─\u001b[0m\u001b[38;5;54m─\u001b[0m\u001b[38;5;54m─\u001b[0m\u001b[38;5;54m─\u001b[0m not found in this scope",
      "\u001b[38;5;246m───╯\u001b[0m"
     ]
    }
   ],
   "source": [
    "let arr11: Array2<f64> = arr9.dot(&identity);\n",
    "println!(\"{}\", arr11);\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "vscode": {
     "languageId": "rust"
    }
   },
   "outputs": [],
   "source": [
    "// 可以声明3维度或更高维度的数组\n",
    "let arr12 = Array::<i8, _>::ones((2, 3, 2, 2));\n",
    "println!(\"MULTIDIMENSIONAL\\n{}\", arr12);\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "首先将ndarray-stats和noisy_float引入交互式编程环境："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "vscode": {
     "languageId": "rust"
    }
   },
   "outputs": [],
   "source": [
    ":dep ndarray-stats = {version=\"0.5.1\"}\n",
    ":dep noisy_float = {version=\"0.2.0\"}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "vscode": {
     "languageId": "rust"
    }
   },
   "outputs": [],
   "source": [
    ":dep ndarray-rand={version = \"0.14.0\"}\n",
    "\n",
    "use ndarray_rand::{RandomExt, SamplingStrategy};\n",
    "use ndarray_rand::rand_distr::Uniform;\n",
    "\n",
    "\n",
    "use ndarray_rand::rand as rand;\n",
    "use rand::seq::IteratorRandom;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "vscode": {
     "languageId": "rust"
    }
   },
   "outputs": [],
   "source": [
    "use ndarray_stats::HistogramExt;\n",
    "use ndarray_stats::histogram::{strategies::Sqrt, GridBuilder};\n",
    "use noisy_float::types::{N64, n64};"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "vscode": {
     "languageId": "rust"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The type of the variable arr3 was redefined, so was lost.\n",
      "The type of the variable arr10 was redefined, so was lost.\n",
      "The type of the variable arr8 was redefined, so was lost.\n"
     ]
    }
   ],
   "source": [
    "use ndarray_rand::rand_distr::{Uniform, StandardNormal}; \n",
    "\n",
    "let mut rng = rand::thread_rng();\n",
    "// 生成一个大小为 10000 × 2 10000 \\times 2 10000×2的矩阵，矩阵中的元素值满足标准正态分布\n",
    "let arr17 = Array::<f64, _>::random_using((10000,2), StandardNormal, &mut rand::thread_rng());\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "将数组中的元素转成noisy float后，我们就可以创建统计直方图了。直方图需要以一定的步长切分数据，在ndarray-stats中称之为grid。ndarray-stats提供了GridBuilder来完成数据切分工作。这里我们使用strategies::Sqrt（很多应用都采用此策略，例如我们熟悉的Excel）来帮我们自动推断最佳的切分步长。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "vscode": {
     "languageId": "rust"
    }
   },
   "outputs": [],
   "source": [
    "// 要对ndarray中每一个元素进行操作，我们可以用mapv()。mapv()跟map()类似，传入一个闭包对每一个迭代值机型操作\n",
    "let data = arr17.mapv(|e| n64(e));\n",
    "\n",
    "let grid = GridBuilder::<Sqrt<N64>>::from_array(&data).unwrap().build();\n",
    "\n",
    "let histogram = data.histogram(grid);\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我们关注的是每个区间内元素的数量，所以我们可以用counts()方法来统计数量"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "vscode": {
     "languageId": "rust"
    }
   },
   "outputs": [
    {
     "ename": "Error",
     "evalue": "`histogram` does not live long enough",
     "output_type": "error",
     "traceback": [
      "\u001b[31m[E0597] Error:\u001b[0m `histogram` does not live long enough",
      "   \u001b[38;5;246m╭\u001b[0m\u001b[38;5;246m─\u001b[0m\u001b[38;5;246m[\u001b[0mcommand_41:1:1\u001b[38;5;246m]\u001b[0m",
      "   \u001b[38;5;246m│\u001b[0m",
      " \u001b[38;5;246m1 │\u001b[0m \u001b[38;5;249ml\u001b[0m\u001b[38;5;249me\u001b[0m\u001b[38;5;249mt\u001b[0m\u001b[38;5;249m \u001b[0m\u001b[38;5;249mh\u001b[0m\u001b[38;5;249mi\u001b[0m\u001b[38;5;249ms\u001b[0m\u001b[38;5;249mt\u001b[0m\u001b[38;5;249mo\u001b[0m\u001b[38;5;249mg\u001b[0m\u001b[38;5;249mr\u001b[0m\u001b[38;5;249ma\u001b[0m\u001b[38;5;249mm\u001b[0m\u001b[38;5;249m_\u001b[0m\u001b[38;5;249mm\u001b[0m\u001b[38;5;249ma\u001b[0m\u001b[38;5;249mt\u001b[0m\u001b[38;5;249mr\u001b[0m\u001b[38;5;249mi\u001b[0m\u001b[38;5;249mx\u001b[0m\u001b[38;5;249m \u001b[0m\u001b[38;5;249m=\u001b[0m\u001b[38;5;249m \u001b[0m\u001b[38;5;54mh\u001b[0m\u001b[38;5;54mi\u001b[0m\u001b[38;5;54ms\u001b[0m\u001b[38;5;54mt\u001b[0m\u001b[38;5;54mo\u001b[0m\u001b[38;5;54mg\u001b[0m\u001b[38;5;54mr\u001b[0m\u001b[38;5;54ma\u001b[0m\u001b[38;5;54mm\u001b[0m\u001b[38;5;100m.\u001b[0m\u001b[38;5;100mc\u001b[0m\u001b[38;5;100mo\u001b[0m\u001b[38;5;100mu\u001b[0m\u001b[38;5;100mn\u001b[0m\u001b[38;5;100mt\u001b[0m\u001b[38;5;100ms\u001b[0m\u001b[38;5;100m(\u001b[0m\u001b[38;5;100m)\u001b[0m\u001b[38;5;249m;\u001b[0m",
      " \u001b[38;5;240m  │\u001b[0m                        \u001b[38;5;54m─\u001b[0m\u001b[38;5;54m─\u001b[0m\u001b[38;5;54m─\u001b[0m\u001b[38;5;54m─\u001b[0m\u001b[38;5;54m┬\u001b[0m\u001b[38;5;54m─\u001b[0m\u001b[38;5;54m─\u001b[0m\u001b[38;5;54m─\u001b[0m\u001b[38;5;54m─\u001b[0m\u001b[38;5;100m┬\u001b[0m\u001b[38;5;100m─\u001b[0m\u001b[38;5;100m─\u001b[0m\u001b[38;5;100m─\u001b[0m\u001b[38;5;100m─\u001b[0m\u001b[38;5;100m─\u001b[0m\u001b[38;5;100m─\u001b[0m\u001b[38;5;100m─\u001b[0m\u001b[38;5;100m─\u001b[0m  ",
      " \u001b[38;5;240m  │\u001b[0m                            \u001b[38;5;54m╰\u001b[0m\u001b[38;5;54m─\u001b[0m\u001b[38;5;54m─\u001b[0m\u001b[38;5;54m─\u001b[0m\u001b[38;5;54m─\u001b[0m\u001b[38;5;54m─\u001b[0m\u001b[38;5;54m─\u001b[0m\u001b[38;5;54m─\u001b[0m\u001b[38;5;54m─\u001b[0m\u001b[38;5;54m─\u001b[0m\u001b[38;5;54m─\u001b[0m\u001b[38;5;54m─\u001b[0m\u001b[38;5;54m─\u001b[0m\u001b[38;5;54m─\u001b[0m\u001b[38;5;54m─\u001b[0m\u001b[38;5;54m─\u001b[0m borrowed value does not live long enough",
      " \u001b[38;5;240m  │\u001b[0m                                 \u001b[38;5;100m│\u001b[0m          ",
      " \u001b[38;5;240m  │\u001b[0m                                 \u001b[38;5;100m╰\u001b[0m\u001b[38;5;100m─\u001b[0m\u001b[38;5;100m─\u001b[0m\u001b[38;5;100m─\u001b[0m\u001b[38;5;100m─\u001b[0m\u001b[38;5;100m─\u001b[0m\u001b[38;5;100m─\u001b[0m\u001b[38;5;100m─\u001b[0m\u001b[38;5;100m─\u001b[0m\u001b[38;5;100m─\u001b[0m\u001b[38;5;100m─\u001b[0m argument requires that `histogram` is borrowed for `'static`",
      "\u001b[38;5;246m───╯\u001b[0m"
     ]
    },
    {
     "ename": "Error",
     "evalue": "cannot move out of `histogram` because it is borrowed",
     "output_type": "error",
     "traceback": [
      "\u001b[31m[E0505] Error:\u001b[0m cannot move out of `histogram` because it is borrowed",
      "   \u001b[38;5;246m╭\u001b[0m\u001b[38;5;246m─\u001b[0m\u001b[38;5;246m[\u001b[0mcommand_41:1:1\u001b[38;5;246m]\u001b[0m",
      "   \u001b[38;5;246m│\u001b[0m",
      " \u001b[38;5;246m1 │\u001b[0m \u001b[38;5;249ml\u001b[0m\u001b[38;5;249me\u001b[0m\u001b[38;5;249mt\u001b[0m\u001b[38;5;249m \u001b[0m\u001b[38;5;249mh\u001b[0m\u001b[38;5;249mi\u001b[0m\u001b[38;5;249ms\u001b[0m\u001b[38;5;249mt\u001b[0m\u001b[38;5;249mo\u001b[0m\u001b[38;5;249mg\u001b[0m\u001b[38;5;249mr\u001b[0m\u001b[38;5;249ma\u001b[0m\u001b[38;5;249mm\u001b[0m\u001b[38;5;249m_\u001b[0m\u001b[38;5;249mm\u001b[0m\u001b[38;5;249ma\u001b[0m\u001b[38;5;249mt\u001b[0m\u001b[38;5;249mr\u001b[0m\u001b[38;5;249mi\u001b[0m\u001b[38;5;249mx\u001b[0m\u001b[38;5;249m \u001b[0m\u001b[38;5;249m=\u001b[0m\u001b[38;5;249m \u001b[0m\u001b[38;5;54mh\u001b[0m\u001b[38;5;54mi\u001b[0m\u001b[38;5;54ms\u001b[0m\u001b[38;5;54mt\u001b[0m\u001b[38;5;54mo\u001b[0m\u001b[38;5;54mg\u001b[0m\u001b[38;5;54mr\u001b[0m\u001b[38;5;54ma\u001b[0m\u001b[38;5;54mm\u001b[0m\u001b[38;5;100m.\u001b[0m\u001b[38;5;100mc\u001b[0m\u001b[38;5;100mo\u001b[0m\u001b[38;5;100mu\u001b[0m\u001b[38;5;100mn\u001b[0m\u001b[38;5;100mt\u001b[0m\u001b[38;5;100ms\u001b[0m\u001b[38;5;100m(\u001b[0m\u001b[38;5;100m)\u001b[0m\u001b[38;5;249m;\u001b[0m",
      " \u001b[38;5;240m  │\u001b[0m                        \u001b[38;5;54m─\u001b[0m\u001b[38;5;54m─\u001b[0m\u001b[38;5;54m─\u001b[0m\u001b[38;5;54m─\u001b[0m\u001b[38;5;54m┬\u001b[0m\u001b[38;5;54m─\u001b[0m\u001b[38;5;54m─\u001b[0m\u001b[38;5;54m─\u001b[0m\u001b[38;5;54m─\u001b[0m\u001b[38;5;100m┬\u001b[0m\u001b[38;5;100m─\u001b[0m\u001b[38;5;100m─\u001b[0m\u001b[38;5;100m─\u001b[0m\u001b[38;5;100m─\u001b[0m\u001b[38;5;100m─\u001b[0m\u001b[38;5;100m─\u001b[0m\u001b[38;5;100m─\u001b[0m\u001b[38;5;100m─\u001b[0m  ",
      " \u001b[38;5;240m  │\u001b[0m                            \u001b[38;5;54m╰\u001b[0m\u001b[38;5;54m─\u001b[0m\u001b[38;5;54m─\u001b[0m\u001b[38;5;54m─\u001b[0m\u001b[38;5;54m─\u001b[0m\u001b[38;5;54m─\u001b[0m\u001b[38;5;54m─\u001b[0m\u001b[38;5;54m─\u001b[0m\u001b[38;5;54m─\u001b[0m\u001b[38;5;54m─\u001b[0m\u001b[38;5;54m─\u001b[0m\u001b[38;5;54m─\u001b[0m\u001b[38;5;54m─\u001b[0m\u001b[38;5;54m─\u001b[0m\u001b[38;5;54m─\u001b[0m\u001b[38;5;54m─\u001b[0m borrow of `histogram` occurs here",
      " \u001b[38;5;240m  │\u001b[0m                                 \u001b[38;5;100m│\u001b[0m          ",
      " \u001b[38;5;240m  │\u001b[0m                                 \u001b[38;5;100m╰\u001b[0m\u001b[38;5;100m─\u001b[0m\u001b[38;5;100m─\u001b[0m\u001b[38;5;100m─\u001b[0m\u001b[38;5;100m─\u001b[0m\u001b[38;5;100m─\u001b[0m\u001b[38;5;100m─\u001b[0m\u001b[38;5;100m─\u001b[0m\u001b[38;5;100m─\u001b[0m\u001b[38;5;100m─\u001b[0m\u001b[38;5;100m─\u001b[0m argument requires that `histogram` is borrowed for `'static`",
      "\u001b[38;5;246m───╯\u001b[0m"
     ]
    }
   ],
   "source": [
    "let histogram_matrix = histogram.counts();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "这里得到的histogram_matrix 是一个计数矩阵，统计了每个grid内元素的数量。我们的直方图一般是按列统计的，所以我们需要将histogram_matrix按有轴累加起来，这样得到的一维数组就是直方图的统计计数了。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "vscode": {
     "languageId": "rust"
    }
   },
   "outputs": [],
   "source": [
    "let data = histogram_matrix.sum_axis(Axis(0));\n",
    "println!(\"{}\", data);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "虽然输出结果不如图像直观，但也能感觉到确实是服从正态分布的。为了让大家直观看到结果，我们来将数据可视化。数据可视化是数据分析和机器学习中非常重要的一块，后面我会专门一张讲解如何在交互环境下用plotters进行数据可视化。在这里主要让大家可视化地看一下结果。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "vscode": {
     "languageId": "rust"
    }
   },
   "outputs": [],
   "source": [
    ":dep plotters = {version=\"0.3.4\", default_features = false, features = [\"evcxr\", \"all_series\", \"all_elements\"]}\n",
    "\n",
    "extern crate plotters;\n",
    "use plotters::prelude::*;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "vscode": {
     "languageId": "rust"
    }
   },
   "outputs": [],
   "source": [
    "let figure = evcxr_figure((640, 480), |root| {\n",
    "    root.fill(&WHITE);\n",
    "    let histogram_matrix = histogram.counts();\n",
    "    let data = histogram_matrix.sum_axis(Axis(0));\n",
    "    let mut chart = ChartBuilder::on(&root)\n",
    "        .set_label_area_size(LabelAreaPosition::Left, 40)\n",
    "        .set_label_area_size(LabelAreaPosition::Bottom, 40)\n",
    "        .caption(\"随机数分布图\", (\"sans-serif\", 30))\n",
    "        .build_cartesian_2d((0..data.len()).into_segmented(), 0..300)\n",
    "        .unwrap();\n",
    "\n",
    "    chart\n",
    "        .configure_mesh()\n",
    "        .disable_x_mesh()\n",
    "        .bold_line_style(&WHITE.mix(0.3))\n",
    "        .axis_desc_style((\"sans-serif\", 15))\n",
    "        .draw()?;\n",
    "\n",
    "    chart.draw_series((0..).zip(data.iter()).map(|(x, y)| {\n",
    "        let x0 = SegmentValue::Exact(x);\n",
    "        let x1 = SegmentValue::Exact(x + 1);\n",
    "        let mut bar = Rectangle::new([(x0, 0), (x1, (*y) as i32)], RED.filled());\n",
    "        bar.set_margin(0, 0, 1, 1);\n",
    "        bar\n",
    "    }))?;\n",
    "\n",
    "    Ok(())\n",
    "});\n",
    "figure\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "vscode": {
     "languageId": "rust"
    }
   },
   "outputs": [],
   "source": []
  }
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